Updated: 2020-08-09 07:14:40 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
South Dakota 1.27 9311 103
Vermont 1.26 1455 5
Virginia 1.19 78829 970
Illinois 1.17 193072 1888
North Dakota 1.17 7454 143
Idaho 1.16 24560 541
Indiana 1.15 74922 979
Arkansas 1.14 47120 851
Kentucky 1.11 35989 689
Rhode Island 1.11 17944 105
Kansas 1.09 31104 436
West Virginia 1.07 7579 133
Wisconsin 1.07 60074 916
Georgia 1.06 194462 3487
Montana 1.05 4816 122
Texas 1.05 504871 8573
Iowa 1.03 48459 483
Minnesota 1.03 59966 724
New Hampshire 1.03 6816 30
New York 1.03 425056 678
Oregon 1.03 21001 329
Utah 1.02 43905 468
Alabama 1.01 100072 1600
Massachusetts 1.00 120420 392
Nebraska 0.99 28203 280
Delaware 0.98 15288 89
Michigan 0.98 96100 709
North Carolina 0.98 134967 1651
Ohio 0.98 100024 1190
Colorado 0.97 50549 473
Tennessee 0.96 117709 1965
Mississippi 0.95 66746 1074
Oklahoma 0.95 43172 871
South Carolina 0.95 99630 1342
Washington 0.95 64588 714
Nevada 0.94 55680 922
California 0.93 555558 6880
Louisiana 0.92 129904 1615
Pennsylvania 0.92 122857 768
New Jersey 0.91 185650 349
Missouri 0.90 52191 958
Florida 0.89 525551 7088
Maryland 0.89 95306 754
Wyoming 0.89 3030 38
New Mexico 0.81 22172 192
Maine 0.76 4045 14
Arizona 0.73 186826 1439
Connecticut 0.57 50166 69

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 24 1 1.8 116 240 5
King WA 1 2 1.0 16469 760 162
Pierce WA 3 3 1.1 6177 720 109
Grant WA 9 4 1.2 1521 1600 34
Clark WA 8 5 1.1 2052 440 34
Walla Walla WA 17 6 1.3 535 890 17
Spokane WA 5 7 0.9 4220 850 64
Snohomish WA 4 8 0.9 6161 780 53
Yakima WA 2 11 0.8 10859 4360 49
Benton WA 6 12 0.8 3875 2000 31
Franklin WA 7 15 0.7 3603 3970 24
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.0 4826 600 68
Marion OR 3 2 1.1 2877 860 39
Malheur OR 6 3 1.2 770 2530 17
Yamhill OR 10 4 1.2 450 430 15
Columbia OR 23 5 1.5 95 190 3
Lincoln OR 11 6 1.4 417 870 4
Wasco OR 18 7 1.4 188 730 4
Washington OR 2 8 0.9 3040 520 39
Clackamas OR 5 9 1.1 1525 380 21
Umatilla OR 4 10 0.9 2271 2950 40
Jackson OR 9 11 1.1 465 220 14
Lane OR 8 12 1.1 577 160 11
Deschutes OR 7 18 1.0 592 330 10
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Del Norte CA 50 1 2.8 100 360 2
Los Angeles CA 1 2 1.0 206321 2040 2510
Riverside CA 2 3 1.1 40754 1710 481
Mendocino CA 39 4 1.7 408 470 18
San Diego CA 5 5 1.1 32235 980 414
Contra Costa CA 15 6 1.2 8917 790 178
Humboldt CA 43 7 1.7 286 210 9
Fresno CA 7 9 0.9 17208 1760 310
Alameda CA 8 10 1.0 12604 770 173
San Bernardino CA 4 13 0.8 35496 1660 390
Orange CA 3 14 0.8 39217 1240 311
Kern CA 6 23 0.6 22520 2550 300
San Joaquin CA 9 32 0.6 12417 1700 101

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.7 126090 2960 982
Pima AZ 2 2 0.9 17702 1740 202
Cochise AZ 11 3 1.1 1636 1300 21
Yuma AZ 3 4 0.7 11540 5550 68
Yavapai AZ 10 5 0.9 2003 890 28
Mohave AZ 6 6 0.8 3186 1550 28
Coconino AZ 8 7 0.9 3080 2200 16
Apache AZ 7 8 0.9 3164 4420 17
Navajo AZ 5 9 0.8 5373 4940 18
Santa Cruz AZ 9 12 0.7 2661 5710 8
Pinal AZ 4 13 0.4 8406 2000 28
CO
county ST case rank severity R_e cases cases/100k daily cases
San Miguel CO 32 1 2.0 86 1080 2
Pueblo CO 13 2 1.5 684 420 14
El Paso CO 4 3 1.1 5127 750 79
Broomfield CO 15 4 1.5 461 700 9
Summit CO 16 5 1.7 338 1110 3
Elbert CO 30 6 1.6 106 420 3
Adams CO 3 7 0.9 6441 1300 63
Weld CO 6 8 1.1 3695 1250 24
Larimer CO 9 10 1.1 1523 450 23
Denver CO 1 11 0.9 10185 1470 70
Jefferson CO 5 12 0.9 4162 730 37
Arapahoe CO 2 14 0.8 7297 1150 50
Boulder CO 7 15 1.0 2068 640 22
Douglas CO 8 19 0.8 1732 530 14
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 20568 1840 189
Utah UT 2 2 1.1 8648 1460 122
Sevier UT 16 3 1.8 83 390 1
Wasatch UT 11 4 1.5 556 1820 5
Washington UT 5 5 1.1 2500 1560 30
Sanpete UT 14 6 1.5 125 430 3
Cache UT 6 7 1.2 1915 1570 15
Davis UT 3 10 0.9 3199 940 34
Weber UT 4 11 0.9 2754 1110 29
Tooele UT 9 12 0.9 584 900 7
San Juan UT 8 14 0.9 652 4270 5
Summit UT 7 16 0.7 709 1750 2
NM
county ST case rank severity R_e cases cases/100k daily cases
Doña Ana NM 4 1 1.1 2430 1130 35
Eddy NM 14 2 1.3 292 510 7
Lea NM 7 3 1.1 763 1090 19
Chaves NM 11 4 1.1 438 670 14
Rio Arriba NM 13 5 1.2 317 810 4
Bernalillo NM 1 6 0.7 5109 750 40
Curry NM 10 7 0.9 544 1080 12
Santa Fe NM 9 9 0.8 640 430 9
San Juan NM 3 13 0.7 3042 2390 7
Sandoval NM 5 15 0.7 1132 800 6
McKinley NM 2 16 0.6 4046 5550 7
Otero NM 6 17 0.6 1102 1680 3
Cibola NM 8 18 0.3 678 2510 7

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Union NJ 6 1 1.6 16821 3040 13
Gloucester NJ 16 2 1.2 3244 1120 24
Middlesex NJ 4 3 1.0 18085 2190 27
Essex NJ 2 4 1.0 19951 2510 30
Hudson NJ 3 5 1.0 19799 2960 21
Bergen NJ 1 6 0.9 20995 2260 35
Monmouth NJ 8 7 0.9 10387 1670 29
Passaic NJ 5 8 0.9 17775 3530 23
Camden NJ 9 12 0.8 8585 1690 29
Ocean NJ 7 15 0.7 10659 1800 21
PA
county ST case rank severity R_e cases cases/100k daily cases
Northumberland PA 29 1 1.9 453 490 10
Mercer PA 30 2 1.5 416 370 13
Huntingdon PA 37 3 1.8 303 670 3
York PA 13 4 1.3 2504 560 33
Cambria PA 35 5 1.4 341 250 13
Union PA 39 6 1.3 227 500 10
Clearfield PA 42 7 1.5 163 200 5
Philadelphia PA 1 10 0.8 31188 1980 106
Allegheny PA 4 12 0.8 8817 720 95
Berks PA 7 15 1.0 5351 1280 26
Delaware PA 3 17 0.8 9223 1640 59
Lancaster PA 6 18 0.9 5858 1090 39
Montgomery PA 2 20 0.9 10068 1230 38
Bucks PA 5 24 0.8 7184 1150 32
Chester PA 8 26 0.8 5126 990 30
Lehigh PA 9 33 0.8 4936 1360 15
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore city MD 4 1 0.9 12412 2020 142
Baltimore MD 3 2 0.9 13122 1590 150
Prince George’s MD 1 3 0.9 24029 2650 139
Montgomery MD 2 4 0.9 18284 1760 85
Anne Arundel MD 5 5 0.9 7326 1290 63
Cecil MD 14 6 1.2 701 680 10
Howard MD 6 7 0.9 3822 1210 34
Charles MD 8 9 1.0 2008 1270 20
Harford MD 9 13 0.8 1951 780 21
Frederick MD 7 18 0.7 3064 1230 9
VA
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg VA 29 1 3.1 407 1320 23
Essex VA 71 2 2.4 97 880 4
Westmoreland VA 48 3 2.4 206 1170 2
Russell VA 70 4 2.1 111 400 10
Patrick VA 61 5 2.1 145 810 8
Scott VA 73 6 2.1 87 400 7
Richmond VA 37 7 2.5 322 3630 1
Prince William VA 2 16 1.2 9390 2060 82
Fairfax VA 1 17 1.2 16292 1420 86
Chesterfield VA 5 21 1.2 4340 1280 55
Loudoun VA 3 25 1.2 5230 1360 34
Norfolk city VA 7 29 1.0 3699 1510 75
Virginia Beach city VA 4 30 0.9 4894 1090 100
Arlington VA 8 34 1.2 3060 1320 23
Newport News city VA 9 45 1.0 1817 1010 30
Henrico VA 6 48 0.9 3838 1180 37
WV
county ST case rank severity R_e cases cases/100k daily cases
Grant WV 22 1 1.8 120 1030 10
Logan WV 10 2 1.6 216 640 15
Boone WV 24 3 1.6 102 450 4
Wayne WV 12 4 1.7 204 500 3
Lincoln WV 26 5 1.5 87 410 4
Mingo WV 16 6 1.3 171 690 7
Greenbrier WV 25 7 1.7 92 260 1
Cabell WV 4 8 1.2 388 410 10
Berkeley WV 3 11 1.1 692 610 7
Harrison WV 9 13 1.1 220 320 5
Kanawha WV 2 14 0.9 913 490 15
Raleigh WV 8 15 1.0 227 300 7
Wood WV 7 16 1.0 246 290 2
Ohio WV 6 21 0.7 269 630 2
Monongalia WV 1 23 0.5 943 900 4
Jefferson WV 5 24 0.8 296 530 1
DE
county ST case rank severity R_e cases cases/100k daily cases
Sussex DE 2 1 1.2 5830 2660 28
New Castle DE 1 2 0.9 7179 1290 47
Kent DE 3 3 1.0 2279 1300 14

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Clarke AL 40 1 2.5 601 2460 26
Washington AL 54 2 1.9 361 2170 12
Jefferson AL 1 3 1.1 13222 2000 246
Talladega AL 23 4 1.2 1259 1560 41
Jackson AL 28 5 1.3 1006 1930 33
Mobile AL 2 6 0.9 9938 2400 191
Pike AL 37 7 1.4 745 2230 15
Montgomery AL 3 8 1.1 6728 2960 80
Tuscaloosa AL 5 12 1.0 4292 2080 55
Marshall AL 8 13 1.1 3210 3370 42
Shelby AL 7 14 1.0 3465 1640 54
Lee AL 9 17 1.0 2823 1770 36
Baldwin AL 6 23 0.9 3567 1710 60
Madison AL 4 27 0.8 5444 1520 70
MS
county ST case rank severity R_e cases cases/100k daily cases
Stone MS 78 1 1.9 189 1030 8
Harrison MS 3 2 1.3 2485 1230 66
Bolivar MS 14 3 1.3 1148 3520 39
Marshall MS 36 4 1.4 687 1920 23
Neshoba MS 12 5 1.4 1296 4410 16
Scott MS 20 6 1.5 999 3520 8
Warren MS 17 7 1.3 1063 2260 21
Jackson MS 5 9 1.0 2292 1610 57
DeSoto MS 2 10 1.0 3658 2080 68
Washington MS 9 16 1.0 1643 3490 30
Forrest MS 8 27 0.9 1782 2360 28
Hinds MS 1 31 0.7 5617 2320 64
Jones MS 7 43 0.8 1886 2760 21
Madison MS 4 47 0.7 2423 2340 21
Rankin MS 6 50 0.7 2279 1510 24
LA
county ST case rank severity R_e cases cases/100k daily cases
West Feliciana LA 53 1 2.0 344 2240 4
Lafayette LA 4 2 1.3 7658 3190 185
St. Landry LA 15 3 1.2 2703 3240 76
Evangeline LA 31 4 1.3 877 2610 24
East Baton Rouge LA 2 5 0.9 12058 2720 159
Jefferson LA 1 6 0.9 15210 3490 120
LaSalle LA 57 7 1.3 288 1930 10
Tangipahoa LA 9 8 1.0 3424 2620 49
Ouachita LA 8 9 1.0 4818 3090 58
St. Tammany LA 7 10 0.9 5121 2030 62
Caddo LA 6 15 0.8 6666 2680 66
Orleans LA 3 28 0.8 10671 2740 47
Calcasieu LA 5 44 0.5 6831 3410 59

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Franklin FL 63 1 3.8 418 3560 79
Baker FL 51 2 3.0 704 2530 83
Dixie FL 59 3 3.1 447 2720 45
Taylor FL 46 4 2.7 1043 4720 141
Gulf FL 56 5 2.2 601 3740 55
Calhoun FL 61 6 2.3 435 3010 29
Washington FL 50 7 1.7 849 3460 52
Miami-Dade FL 1 12 0.7 131263 4830 1508
Hillsborough FL 4 15 0.9 32323 2340 382
Duval FL 6 16 1.0 23133 2500 270
Polk FL 9 19 1.0 14078 2110 202
Palm Beach FL 3 20 0.8 36603 2530 406
Broward FL 2 23 0.7 61725 3230 680
Orange FL 5 25 0.8 31376 2370 289
Pinellas FL 7 29 0.8 17739 1850 169
Lee FL 8 33 0.8 16405 2280 129
GA
county ST case rank severity R_e cases cases/100k daily cases
Bleckley GA 131 1 2.0 187 1460 10
Jasper GA 136 2 2.0 159 1150 7
Cherokee GA 12 3 1.4 3358 1390 96
Cobb GA 4 4 1.2 13319 1790 295
Fulton GA 1 5 1.1 19971 1950 334
DeKalb GA 3 6 1.1 13701 1840 222
Gwinnett GA 2 7 1.0 19537 2170 322
Chatham GA 6 14 1.1 5662 1970 116
Hall GA 5 17 1.1 6034 3080 91
Clayton GA 7 26 1.0 4945 1770 75
Muscogee GA 8 27 1.0 4664 2370 64
Richmond GA 9 37 0.9 4226 2100 94

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Hamilton TX 180 1 4.1 77 930 6
Winkler TX 177 2 3.7 84 1080 4
Karnes TX 80 3 2.9 637 4140 84
Medina TX 65 4 3.0 758 1540 41
La Salle TX 101 5 3.3 362 4890 1
Bee TX 57 6 2.5 975 2980 101
Fort Bend TX 10 7 2.0 9463 1280 406
Tarrant TX 4 14 1.2 33417 1650 730
Harris TX 1 19 1.0 85030 1850 1465
Hidalgo TX 6 20 1.2 19688 2320 384
Cameron TX 7 21 1.0 17276 4100 739
El Paso TX 8 28 1.1 16096 1920 240
Nueces TX 9 32 1.0 14150 3930 270
Travis TX 5 33 1.0 22706 1890 238
Dallas TX 2 39 0.8 53904 2080 458
Bexar TX 3 77 0.5 42608 2210 241
OK
county ST case rank severity R_e cases cases/100k daily cases
Pittsburg OK 30 1 2.3 322 730 30
Tulsa OK 2 2 1.1 10378 1610 226
Oklahoma OK 1 3 0.9 10491 1340 198
McIntosh OK 40 4 1.5 181 910 6
Choctaw OK 39 5 1.6 181 1220 3
Washington OK 11 6 1.3 623 1200 9
Bryan OK 17 7 1.2 442 970 12
Rogers OK 6 9 1.0 961 1060 28
Wagoner OK 8 10 1.0 849 1090 21
Texas OK 5 11 1.3 1054 4990 3
Cleveland OK 3 18 0.7 3004 1090 50
Canadian OK 4 19 0.8 1200 880 22
McCurtain OK 7 36 0.8 854 2590 5
Comanche OK 9 47 0.5 807 660 5

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Bay MI 21 1 1.8 612 580 13
Alpena MI 45 2 2.3 129 450 0
Barry MI 38 3 2.1 178 300 2
Macomb MI 3 4 1.1 10556 1220 122
Oakland MI 2 5 1.0 15401 1230 109
Menominee MI 43 6 1.5 132 570 7
Wayne MI 1 7 0.9 28132 1600 136
Saginaw MI 8 8 1.2 1982 1030 23
Kent MI 4 9 0.9 7503 1170 53
Washtenaw MI 6 16 1.0 3041 830 21
Genesee MI 5 22 0.9 3651 890 24
Ottawa MI 9 26 0.8 1820 640 13
Jackson MI 7 48 0.4 2435 1530 4
WI
county ST case rank severity R_e cases cases/100k daily cases
Green WI 39 1 2.4 153 420 5
Lafayette WI 43 2 1.9 127 760 4
Oneida WI 45 3 1.8 119 340 7
Door WI 47 4 1.9 105 380 3
Portage WI 23 5 1.7 399 570 10
Iowa WI 50 6 1.9 77 330 3
Kewaunee WI 42 7 1.9 129 630 4
Milwaukee WI 1 11 1.0 20929 2190 212
Brown WI 3 13 1.2 4233 1630 42
Waukesha WI 4 14 1.0 4143 1040 92
Racine WI 5 16 1.1 3495 1790 49
Dane WI 2 19 1.1 4492 850 54
Outagamie WI 9 26 1.1 1232 670 24
Kenosha WI 6 29 1.0 2667 1580 32
Rock WI 7 34 1.0 1557 960 11
Walworth WI 8 44 0.7 1324 1290 14

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
McLeod MN 35 1 2.1 172 480 8
Hennepin MN 1 2 1.0 19050 1540 217
Jackson MN 55 3 1.9 78 780 2
Isanti MN 45 4 1.8 124 320 3
Ramsey MN 2 5 1.1 7427 1370 98
Dakota MN 3 6 1.0 4330 1040 70
Anoka MN 4 7 1.1 3611 1040 50
Scott MN 9 10 1.1 1535 1070 30
Washington MN 6 11 1.1 2076 820 33
Olmsted MN 8 12 1.2 1716 1120 18
Stearns MN 5 23 0.9 2888 1840 12
Nobles MN 7 28 1.1 1760 8060 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Hughes SD 15 1 2.5 91 520 1
Yankton SD 10 2 2.0 111 490 2
Meade SD 16 3 1.8 91 330 3
Roberts SD 18 4 1.8 79 770 2
Brookings SD 7 5 1.6 132 390 3
Charles Mix SD 12 6 2.0 102 1090 0
Minnehaha SD 1 7 1.0 4378 2340 28
Pennington SD 2 8 1.1 884 810 9
Codington SD 8 9 1.4 130 460 2
Brown SD 5 10 1.2 435 1120 5
Lincoln SD 3 11 0.8 622 1130 9
Union SD 6 12 1.0 214 1410 3
Clay SD 9 13 0.9 126 900 1
Beadle SD 4 16 0.8 590 3210 0
ND
county ST case rank severity R_e cases cases/100k daily cases
Morton ND 4 1 1.6 346 1130 14
Stark ND 6 2 1.3 259 840 11
Burleigh ND 2 3 1.1 1142 1220 32
Williams ND 5 4 1.3 268 790 7
Cass ND 1 5 1.1 3021 1730 18
Ward ND 7 6 1.2 219 320 7
Grand Forks ND 3 7 1.1 671 950 7
Stutsman ND 9 12 0.8 130 620 2
Benson ND 8 13 0.4 138 2000 3

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 0.8 13158 1530 14
Middlesex CT 6 2 1.0 1400 860 2
Fairfield CT 1 3 0.5 17978 1900 21
New London CT 5 4 0.8 1438 530 4
Windham CT 8 5 0.8 732 630 4
Hartford CT 3 6 0.5 12783 1430 16
Tolland CT 7 7 0.6 1065 700 4
Litchfield CT 4 8 0.5 1612 880 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Essex MA 3 1 1.1 17775 2280 71
Suffolk MA 2 2 1.1 21785 2750 76
Middlesex MA 1 3 1.0 26339 1650 80
Norfolk MA 5 4 0.9 10612 1520 45
Bristol MA 6 5 0.9 9336 1670 32
Worcester MA 4 6 0.8 13602 1650 33
Plymouth MA 7 7 1.0 9232 1800 18
Hampden MA 8 8 0.9 7584 1620 21
Barnstable MA 9 10 0.8 1800 840 7
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.1 15112 2380 84
Kent RI 2 2 1.2 1504 920 12
Newport RI 4 3 1.2 399 480 3
Washington RI 3 4 1.2 610 480 3
Bristol RI 5 5 1.0 319 650 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Allegany NY 51 1 2.6 80 170 1
New York City NY 1 2 1.1 232228 2750 311
Chemung NY 41 3 2.0 170 200 1
Niagara NY 14 4 1.5 1492 700 8
Suffolk NY 2 5 1.0 43751 2940 67
Erie NY 7 6 1.1 8840 960 45
Nassau NY 3 7 1.0 43635 3220 52
Rockland NY 5 8 1.3 13934 4300 9
Monroe NY 8 10 1.0 4926 660 27
Westchester NY 4 11 1.0 36171 3730 30
Dutchess NY 9 17 1.0 4607 1570 13
Orange NY 6 22 0.9 11164 2950 10

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Windham VT 3 1 2.7 103 240 0
Rutland VT 4 2 1.7 100 170 2
Chittenden VT 1 3 1.0 728 450 1
Bennington VT 5 4 0.8 86 240 0
Franklin VT 2 5 0.6 119 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.9 2082 720 5
Androscoggin ME 3 2 0.9 558 520 2
York ME 2 3 0.7 673 330 3
Penobscot ME 5 4 0.7 154 100 1
Kennebec ME 4 5 0.5 172 140 0
NH
county ST case rank severity R_e cases cases/100k daily cases
Strafford NH 4 1 1.5 353 280 4
Rockingham NH 2 2 1.1 1687 550 9
Hillsborough NH 1 3 0.9 3840 930 13
Belknap NH 5 4 1.1 117 190 1
Merrimack NH 3 5 1.1 464 310 1
Cheshire NH 7 6 1.0 99 130 1
Carroll NH 8 7 0.8 95 200 1
Grafton NH 6 8 0.0 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 8716 2130 129
Spartanburg SC 8 2 1.1 4060 1340 54
York SC 9 3 1.1 3529 1360 60
Aiken SC 15 4 1.1 1858 1110 50
Greenville SC 2 5 0.9 10840 2170 104
Georgetown SC 17 6 1.2 1433 2330 29
Dorchester SC 11 7 1.1 3067 1970 49
Horry SC 4 10 0.9 8552 2660 75
Charleston SC 1 11 0.8 12235 3100 104
Beaufort SC 7 12 0.9 4070 2230 74
Berkeley SC 6 20 0.9 4140 1980 46
Lexington SC 5 24 0.8 4936 1720 46
NC
county ST case rank severity R_e cases cases/100k daily cases
Northampton NC 69 1 2.4 315 1560 10
Sampson NC 24 2 1.7 1480 2330 12
Wilkes NC 43 3 1.6 786 1150 11
Stanly NC 37 4 1.4 1044 1710 28
Haywood NC 60 5 1.4 419 690 20
Mecklenburg NC 1 6 0.9 22107 2100 202
Wake NC 2 7 1.0 11982 1140 144
Gaston NC 6 10 1.1 3303 1530 52
Union NC 8 11 1.1 3072 1360 47
Guilford NC 4 12 1.0 5568 1060 64
Forsyth NC 5 15 1.0 5200 1400 50
Cumberland NC 9 21 1.0 3048 920 54
Durham NC 3 34 0.9 6123 2000 41
Johnston NC 7 38 0.8 3284 1720 40

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Flathead MT 4 1 1.3 330 340 14
Yellowstone MT 1 2 1.1 1254 790 32
Missoula MT 5 3 1.3 328 280 11
Big Horn MT 3 4 0.9 424 3170 15
Silver Bow MT 9 5 1.1 87 250 4
Ravalli MT 11 6 1.1 82 200 2
Lewis and Clark MT 8 7 1.0 159 240 4
Gallatin MT 2 8 0.7 939 900 10
Cascade MT 7 10 0.7 167 200 3
Lake MT 6 11 0.7 181 610 2
WY
county ST case rank severity R_e cases cases/100k daily cases
Carbon WY 10 1 1.3 100 650 4
Park WY 7 2 1.3 134 460 3
Lincoln WY 9 3 1.3 102 540 2
Fremont WY 1 4 1.0 507 1270 4
Laramie WY 2 5 0.7 503 510 5
Uinta WY 4 6 0.7 277 1340 2
Campbell WY 8 7 0.9 123 260 1
Natrona WY 6 8 0.7 232 290 2
Sweetwater WY 5 9 0.6 259 590 2
Teton WY 3 10 0.4 376 1630 3
ID
county ST case rank severity R_e cases cases/100k daily cases
Nez Perce ID 21 1 2.0 152 380 4
Canyon ID 2 2 1.3 5719 2690 154
Teton ID 24 3 1.8 85 770 4
Bonneville ID 5 4 1.4 1008 900 51
Ada ID 1 5 1.1 8880 1990 150
Madison ID 20 6 1.6 160 410 5
Shoshone ID 23 7 1.5 94 750 5
Twin Falls ID 4 8 1.1 1371 1640 26
Kootenai ID 3 10 1.0 1797 1170 36
Jerome ID 9 15 1.1 475 2030 8
Cassia ID 7 19 0.9 524 2220 7
Minidoka ID 8 21 0.8 480 2330 6
Blaine ID 6 24 0.9 577 2620 1

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Belmont OH 29 1 2.9 614 900 6
Lawrence OH 45 2 1.8 273 450 12
Darke OH 38 3 1.7 391 760 14
Champaign OH 58 4 1.5 177 460 11
Franklin OH 1 5 1.0 18187 1430 183
Cuyahoga OH 2 6 1.0 13411 1070 132
Logan OH 64 7 1.6 152 340 6
Hamilton OH 3 9 1.0 9568 1180 82
Summit OH 6 16 1.0 3530 650 46
Mahoning OH 9 17 1.1 2527 1090 22
Butler OH 8 19 1.0 2894 770 36
Lucas OH 4 21 0.8 5319 1230 75
Montgomery OH 5 26 0.8 4298 810 50
Marion OH 7 38 1.0 2926 4480 8
IL
county ST case rank severity R_e cases cases/100k daily cases
Jersey IL 58 1 2.6 101 460 8
Jefferson IL 35 2 2.4 263 690 18
LaSalle IL 18 3 1.8 727 660 41
Cook IL 1 4 1.1 110669 2120 711
Woodford IL 50 5 1.9 147 380 10
DuPage IL 3 6 1.2 12142 1300 116
Will IL 5 7 1.2 9174 1330 99
Kane IL 4 10 1.1 9752 1840 84
Lake IL 2 11 1.1 12598 1790 100
Madison IL 9 14 1.2 2524 950 62
St. Clair IL 6 15 1.1 4357 1650 73
McHenry IL 8 28 1.1 3202 1040 39
Winnebago IL 7 51 0.8 3777 1320 16
IN
county ST case rank severity R_e cases cases/100k daily cases
Sullivan IN 75 1 2.7 112 540 8
Putnam IN 43 2 2.4 292 780 13
Carroll IN 58 3 2.1 181 910 9
Clinton IN 36 4 2.0 426 1320 14
Spencer IN 70 5 2.0 134 650 4
Pulaski IN 79 6 2.2 81 640 1
Vigo IN 30 7 1.6 614 570 29
Lake IN 2 9 1.3 7530 1550 80
Marion IN 1 10 1.1 15822 1680 168
St. Joseph IN 5 13 1.2 3428 1270 56
Hendricks IN 8 14 1.3 1886 1170 22
Allen IN 4 18 1.1 3852 1040 41
Hamilton IN 6 20 1.1 2738 870 41
Elkhart IN 3 22 1.1 4884 2400 38
Vanderburgh IN 7 25 1.0 1928 1060 40
Cass IN 9 41 1.2 1785 4690 6

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
White TN 61 1 2.1 280 1050 18
Lake TN 25 2 2.0 816 10840 12
Overton TN 74 3 2.0 177 800 10
Weakley TN 52 4 1.6 426 1270 33
Johnson TN 63 5 1.6 278 1560 24
Jackson TN 80 6 1.9 121 1040 3
Benton TN 77 7 1.6 150 930 12
Shelby TN 1 10 0.8 23316 2490 292
Davidson TN 2 14 0.9 22674 3310 192
Knox TN 5 19 0.9 4759 1040 109
Wilson TN 8 20 1.1 2260 1700 36
Sumner TN 7 22 1.1 3399 1890 42
Hamilton TN 4 23 0.9 6045 1690 75
Williamson TN 6 30 1.0 3492 1600 44
Rutherford TN 3 31 0.8 6470 2110 73
Montgomery TN 9 34 1.0 1906 970 36
KY
county ST case rank severity R_e cases cases/100k daily cases
Carroll KY 42 1 2.0 156 1460 3
Jefferson KY 1 2 1.2 8083 1050 175
Fayette KY 2 3 1.2 3833 1200 96
Nelson KY 35 4 1.7 225 500 6
Madison KY 14 5 1.5 482 540 18
Henry KY 52 6 1.7 122 770 6
Washington KY 71 7 1.7 80 670 4
Shelby KY 7 11 1.4 758 1620 10
Warren KY 3 15 1.0 2619 2070 32
Boone KY 5 21 1.1 1095 850 14
Daviess KY 6 31 1.0 763 760 9
Kenton KY 4 33 0.9 1402 850 15
Muhlenberg KY 8 47 1.1 645 2080 2
Christian KY 9 50 0.7 632 870 8

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Christian MO 23 1 1.6 366 430 16
Stone MO 50 2 1.6 127 400 8
Greene MO 6 3 1.3 1497 520 43
Pike MO 55 4 1.7 96 520 5
Clinton MO 62 5 1.7 80 390 3
St. Louis city MO 2 6 1.0 5149 1650 92
St. Louis MO 1 7 0.8 14801 1480 241
Jackson MO 4 8 0.8 3977 570 84
St. Charles MO 3 9 0.9 4108 1050 72
Jefferson MO 5 10 1.0 1757 790 43
Boone MO 7 11 1.0 1385 780 26
Jasper MO 8 38 0.6 1248 1050 8
Buchanan MO 9 48 0.7 1080 1210 3
AR
county ST case rank severity R_e cases cases/100k daily cases
Jackson AR 59 1 5.5 95 550 13
Clay AR 52 2 2.6 130 860 5
Poinsett AR 33 3 2.3 257 1070 22
Logan AR 34 4 1.7 255 1170 17
Cleveland AR 57 5 1.9 97 1180 5
Pulaski AR 2 6 1.3 5466 1390 108
Craighead AR 8 7 1.4 1333 1260 40
Sebastian AR 4 12 1.1 2152 1690 63
Jefferson AR 6 18 1.1 1508 2140 29
Hot Spring AR 5 21 1.3 1517 4530 12
Crittenden AR 7 23 1.1 1356 2770 24
Washington AR 1 28 0.9 6281 2750 43
Benton AR 3 31 0.9 4748 1830 36
Pope AR 9 37 0.9 1309 2060 17

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 2339.3 seconds to compute.
2020-08-09 07:53:39

version history

Today is 2020-08-09.
81 days ago: Multiple states.
73 days ago: \(R_e\) computation.
70 days ago: created color coding for \(R_e\) plots.
65 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
65 days ago: “persistence” time evolution.
58 days ago: “In control” mapping.
58 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
50 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
45 days ago: Added Per Capita US Map.
43 days ago: Deprecated national map.
39 days ago: added state “Hot 10” analysis.
34 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
32 days ago: added per capita disease and mortaility to state-level analysis.
20 days ago: changed to county boundaries on national map for per capita disease.
15 days ago: corrected factor of two error in death trend data.
11 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
6 days ago: added county level “baseline control” and \(R-e\) maps.
2 days ago: fixed normalization error on total disease stats plot.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.